Donated on 10/31/1988

From G.Gong: CMU; Mostly Boolean or numeric-valued attribute types; Includes cost data (donated by Peter Turney)

Dataset Characteristics


Subject Area

Health and Medicine

Associated Tasks


Feature Type

Categorical, Integer, Real

# Instances


# Features


Dataset Information

Additional Information

Please ask Gail Gong for further information on this database.

Has Missing Values?


Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
Liver BigFeatureCategoricalyes
Liver FirmFeatureCategoricalyes

0 to 10 of 20

Additional Variable Information

1. Class: DIE, LIVE 2. AGE: 10, 20, 30, 40, 50, 60, 70, 80 3. SEX: male, female 4. STEROID: no, yes 5. ANTIVIRALS: no, yes 6. FATIGUE: no, yes 7. MALAISE: no, yes 8. ANOREXIA: no, yes 9. LIVER BIG: no, yes 10. LIVER FIRM: no, yes 11. SPLEEN PALPABLE: no, yes 12. SPIDERS: no, yes 13. ASCITES: no, yes 14. VARICES: no, yes 15. BILIRUBIN: 0.39, 0.80, 1.20, 2.00, 3.00, 4.00 -- see the note below 16. ALK PHOSPHATE: 33, 80, 120, 160, 200, 250 17. SGOT: 13, 100, 200, 300, 400, 500, 18. ALBUMIN: 2.1, 3.0, 3.8, 4.5, 5.0, 6.0 19. PROTIME: 10, 20, 30, 40, 50, 60, 70, 80, 90 20. HISTOLOGY: no, yes The BILIRUBIN attribute appears to be continuously-valued. I checked this with the donater, Bojan Cestnik, who replied: About the hepatitis database and BILIRUBIN problem I would like to say the following: BILIRUBIN is continuous attribute (= the number of it's "values" in the ASDOHEPA.DAT file is negative!!!); "values" are quoted because when speaking about the continuous attribute there is no such thing as all possible values. However, they represent so called "boundary" values; according to these "boundary" values the attribute can be discretized. At the same time, because of the continious attribute, one can perform some other test since the continuous information is preserved. I hope that these lines have at least roughly answered your question.

Baseline Model Performance

Papers Citing this Dataset

Analyzing performance of classifiers for medical datasets

By Rosaida Rosly, Mokhairi Makhtar, Mohd Awang, Mohd Awang, Mohd Rahman. 2018

Published in International Journal of Engineering & Technology.

Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data

By Mateusz Lango, Jerzy Stefanowski. 2017

Published in Journal of Intelligent Information Systems.

RatingScaleReduction package: stepwise rating scale item reduction without predictability loss

By Waldemar Koczkodaj, Alicja Wolny-Dominiak. 2017

Published in The R Journal.

Reliable Confidence Predictions Using Conformal Prediction

By Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström. 2016

Published in PAKDD.

Imbalanced Learning Based on Logistic Discrimination

By Huaping Guo, Weimei Zhi, Hongbing Liu, Mingliang Xu. 2016

Published in Computational intelligence and neuroscience.

0 to 5 of 12

12 citations


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